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Mapping Social Choice Theory to RLHF
We analyze the problem settings of social choice and RLHF, identify key differences between them, and discuss how these differences may affect the RLHF interpretation of well-known technical results in social choice.
Can Probabilistic Feedback Drive User Impacts in Online Platforms?
A common explanation for negative user impacts of content recommender systems is misalignment between the platform's objective and user welfare.
Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations
We then leverage these properties to propose a novel evaluation framework which can quantitatively measure disparities in the quality of explanations output by state-of-the-art methods.
Repairing Regressors for Fair Binary Classification at Any Decision Threshold
We study the problem of post-processing a supervised machine-learned regressor to maximize fair binary classification at all decision thresholds.
What will it take to generate fairness-preserving explanations?
In situations where explanations of black-box models may be useful, the fairness of the black-box is also often a relevant concern.
Fair Machine Learning Under Partial Compliance
If k% of employers were to voluntarily adopt a fairness-promoting intervention, should we expect k% progress (in aggregate) towards the benefits of universal adoption, or will the dynamics of partial compliance wash out the hoped-for benefits?
